DoorDash Data Scientist Interview Questions

Behavioral Questions (15)

1. 1.Tell me about a time you went above and beyond to help a teammate or partner succeed, even when it wasn't directly your responsibility.

   easy~3 min

   What interviewers look for

   • Demonstrates Hustle and Heart by showing genuine care for teammate success beyond formal responsibilities
   • Shows proactive identification of ways to help rather than waiting to be asked
   • Involves concrete actions with measurable impact on the teammate or partner's success
   • Reflects DoorDash's 'We are One Team' value through cross-functional or cross-team collaboration

   Likely follow-ups

   • How did you recognize that your teammate needed help in the first place?
   • What was the impact on your own work or deadlines when you took on this additional effort?
   • How has this experience influenced how you approach teamwork since then?

   Company context

   DoorDash's Hustle and Heart principle combines intense work ethic with genuine care for people - teammates, Dashers, merchants, and consumers. The company's 'We are One Team' value emphasizes that success requires tight collaboration across Engineering, Product, Operations, and external partners, making peer support and proactive helpfulness essential cultural behaviors.

2. 2.You need to design an experiment to test whether showing estimated delivery fees upfront increases order completion rates. Walk me through how you'd set this up, considering our three-sided marketplace.

   easy~3 min

   What interviewers look for

   • Considers impact on all three sides: consumers (transparency vs sticker shock), Dashers (order volume changes), and merchants (conversion effects)
   • Proposes proper experimental design with treatment/control groups, statistical power calculations, and guardrail metrics
   • Identifies potential confounding variables like order time, geography, or merchant type that could skew results

   Likely follow-ups

   • How would you handle the fact that delivery fees vary by distance and demand in real-time?
   • What would you do if the experiment shows higher conversion but lower average order value?

   Company context

   DoorDash's Three-Sided Marketplace Thinking principle requires data scientists to consider how experiments affect consumers, Dashers, and merchants simultaneously. This question tests whether candidates understand marketplace dynamics and can design experiments that don't optimize one side at the expense of others.

3. 3.Design a real-time system that predicts which restaurants will run out of popular items in the next hour, considering we have 500k+ active merchants during peak hours. How would you handle the data pipeline and model serving at this scale?

   easy~3 min

   What interviewers look for

   • Proposes streaming architecture (Kafka/Flink) to process order events in real-time with sub-minute latency requirements
   • Considers merchant-specific patterns and seasonal variations in inventory depletion rates
   • Designs fallback mechanisms when prediction service is down to avoid breaking the ordering experience

   Likely follow-ups

   • How would you handle the cold start problem for new restaurants with no historical data?
   • What would you do if the model starts predicting stockouts incorrectly during a major sports event?

   Company context

   DoorDash's 'Operate at Speed' principle requires real-time inventory management to prevent poor consumer experiences from ordering unavailable items. The three-sided marketplace means incorrect predictions hurt consumers (wasted time), merchants (lost revenue), and Dashers (cancelled orders after pickup attempts).

4. 4.Tell me about a time you had to convince stakeholders to change course on a data science project when your analysis showed the original approach wouldn't work. How did you handle pushback?

   easy~3 min

5. 5.A merchant complains their orders have been taking 20% longer to get picked up over the past month, but our overall pickup metrics look normal. How would you investigate this?

   easy~3 min

6. 6.Tell me about a time when you had to balance conflicting metrics between different user groups in your analysis. How did you approach the tradeoffs?

   medium~4 min

7. 7.Our dispatch algorithm is assigning orders to Dashers, but we're seeing complaints about unfair distribution of high-tip orders. How would you investigate this and design a solution?

   medium~4 min

8. 8.We want to build a dynamic surge pricing model for delivery fees that responds to real-time demand and Dasher availability. Design the data infrastructure and modeling approach, considering we process 30M+ orders daily across different geographic markets.

   medium~4 min

9. 9.Describe a situation where you had to lead an analysis that required coordinating data from multiple teams or systems, but you had no formal authority over those teams. How did you get what you needed?

   medium~4 min

10. 10.Estimate how many additional Dashers we'd need to hire if we expanded our 30-minute delivery promise from metropolitan areas to all suburban markets. Walk me through your calculation.

    medium~4 min

11. 11.Describe a time when you had to deliver critical insights under a tight deadline that directly impacted real-time operations. Walk me through your approach.

    hard~5 min

12. 12.DashPass subscribers have 15% higher lifetime value but also 3x higher support ticket volume. Design a model to predict which potential subscribers will become high-maintenance users before they sign up.

    hard~5 min

13. 13.Design an ML system that detects and prevents fraudulent merchant behavior across our platform, where we need to protect both consumers and legitimate merchants while processing millions of transactions daily. How would you balance false positives against fraud detection accuracy?

    hard~5 min

14. 14.Tell me about a time when you had to present analysis that showed one part of DoorDash's business was performing well while another was struggling, and different executives had competing priorities based on your findings.

    hard~5 min

15. 15.We're seeing a 12% increase in order cancellations during lunch rush, but only on orders placed through our white-label Drive API. The Drive team says their merchant partners haven't changed anything. How would you approach this investigation?

    hard~5 min